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Different from traditional tedious CPU-GPU-based training algorithms using gradient descent methods, the software-FPGA co-designed learning algorithm is created to quickly solve a system of linear equations to directly calculate optimal values of hyperparameters of the green granular neural network (GGNN). To reduce both CO2 emissions and energy consumption effectively, a novel green granular convolutional neural network (GGCNN) is developed by using a new classifier that uses GGNNs as building blocks with new fast software-FPGA co-designed learning. Initial simulation results indicate that the FPGA equation solver code runs faster than the Python equation solver code. Therefore, implementing the GGCNN with software-FPGA co-designed learning is feasible. In the future, The GGCNN will be evaluated by comparing with a convolutional neural network with the traditional software-CPU-GPU-based learning in terms of speeds, model sizes, accuracy, CO2 emissions and energy consumption by using popular datasets. New algorithms will be created to divide the inputs to different input groups for building different GGNNs to solve the curse of dimensionality. 

A novel green granular neural network (GGNN) with new fast software-FPGA co-designed learning is developed to reduce both CO2 emissions and energy consumption more effectively than popular neural networks with the traditional software-CPU-GPU-based learning. Different from traditional tedious CPU-GPU-based training algorithms using gradient descent methods and other methods such as genetic algorithms , the software-FPGA co-designed training algorithm may quickly solve a system of linear equations to directly calculate optimal values of hyperparameters of the GGNN. Initial simulation results indicates that the FPGA equation solver code ran faster than the Python equation solver code. Therefore, implementing the GGNN with software-FPGA co-designed learning is feasible. In addition, the shallow high-speed GGNN is explainable because it can generate interpretable granular If-Then rules. In the future, The GGNN will be evaluated by comparing with other machine learning models with traditional software-based learning in terms of speeds, model sizes, accuracy, CO2 emissions and energy consumption by using popular datasets. New algorithms will be created to divide the inputs to different input groups that will be used to build different small-size GGNNs to solve the curse of dimensionality. Additionally, the explainable green granular convolutional neural network will be developed by using the GGNNs as basic building blocks to efficiently solve image recognition problems.